Aviation's prevalence and use for consumer and commercial uses is well-known. The noted parties are not always able to drive to their specific destination due to time or geographic constraints. This mode of transportation is also used for food movement, time sensitive materials, and the mail system partially. These uses and many more present clear evidence for aviation's pertinence in our daily lives.
As this is integral, the civilization, the underlying efficiency and operational effectiveness factor are likewise pertinent to review. When there is a process or methodology to implement, which would increase the operational efficiencies for the company and improve the user's experience, after vetting, this may be implemented. One such tool is to implement machine learning (ML) to a greater extent and AI into the process. This is in the early stages of implementation as AI improves and becomes operational in the implementation. The potential uses are vast, however there is a significant amount of testing to complete prior to any significant uses.
Modes of Assistance
ML and AI have many uses for this industry. In particular, the automation economizes the processes, producing better experiences for the consumer, client, and business.
This has been in use within the airports at the self-check in at the airports. This available function is exceptionally useful for the busy consumer or business traveler. This implemented tool is quick and efficient, and decreases the direct costs associated with the initial step of the travel process.
Delta Airlines have taken this endeavor to a new level (Pal, 2018). The airline is working to implement an AI experience for their clients. The customer would experience a fully automated check-in system. From the beginning of the process, verifying the client's identification, and subsequent tasks. The client would not need to interact with a Delta Airlines employee, unless they wanted to. This experiment began at the Minneapolis-St. Paul airport in the US. This was engineered to streamline the process, and is an effective way to engage with clients.
American Airlines also has been focused on this and conducted a competition focused on making baggage screening an easier, more pleasant process. The airline titled the contest as HackWars.
A third airline, Airbus, also is taking action with this paradigm shift. ML and AI may be applied to analyzing social media as it applies to the airline. The entries here may be exceptionally enlightening. With the analysis, these would provide insight into the user's experience with their entire journey (Lopez, 2017).
Potential Issues
The application of ML and AI is newer to this industry. The full implementation will be a long road with significant issues to overcome. With these generalized use case, there cannot be an error. The seemingly not significant error for other industries may kill persons in the airline industry.
Data Management
The aircrafts presently generate a mass amount of data (Pal, 2018; Basulto, 2018). The volume of data will increase exponentially with AI implementation. Every particular data point for each flight including not only the GPS data, but also the equipment’s data through the airplane, would be recorded. This vast amount of data has to be secured. As this relates to consumers, the airline cannot allow the consumer’s PII to be compromised. There are laws presently enacted and being enacted in the US address this. In the EU, the GDPR is presently in place and used fully addresses this and allows for fines.
Investment
The implementation of ML and AI will not be inexpensive. To develop this to an acceptable level to be applied will require time and staff experienced in the industry. This is not a short, one year, process. This is the organic method of growth. Also with the staffing, less experienced persons may need to be hired and their skill level grown with time.
The organization may also purchase the intellectual property and expertise. Clearly this requires less time and effort to arrive at nearly the same level of expertise and the competitors. With this avenue the trade-off is the cost. An example of the purchase occurred in 2016 when GE purchased Bit Stew Systems and Wise.io (Scott, 2016). The intent was for the acquisition to expand the platform. This is used for industrial oriented applications to assist the communication between rather large machine and analytic software.
Boeing also followed this route with their investment in Spark Cognition. This business, based in Texas, has a focus in ML and AI.
Information and Cybersecurity
The systems involved would need to be 99.99999% secure. There may not be vulnerabilities involved with the systems managing the process. Any issues would be, to say the least, problematic. One successful compromise would be a disaster across the systems.
Utopian Future
The use case for incorporating AI with the aviation industry is promising. The automation added to the intuitiveness of AI certainly would be a benefit for both the airline and the consumer with various attributes. With a perfectly integrated system, the process from check-in to the destination tarmac would be fully automated, without the opportunity for human interaction, unless requested by a client. From the administrative side, the system would check the environmental conditions through the flight path, optimal flight paths for time and fuel efficiency to reach the break-even point, and meal selection. The staffing for the process would need to be minimal.
Although this seems to be the perfect situation, there is a long path to even get close to this. There are many speedbumps to be overcome with this.
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